Eeg-based methods for measuring psychoactive effects

Abstract

One or more algorithms are used to, in respect of each of a plurality of EEG snippets from a subject, allocate to each snippet a class selected from one or more classes, each class being associated with drug impairment level. The one or more algorithms are each defined by a model and a set of parameters and have been previously trained with a plurality of labelled EEG snippets, the labelled snippets including, for each of the classes, at least one snippet associated with a test subject experiencing the level of drug impairment associated with said each class. A statistical model is created which provides desired levels of sensitivity and accuracy based upon the proportion of snippets from test subjects having the known level of drug effect that falls within the associated class. Drug effect is defined by applying the statistical model to the classes allocated to the snippets.

1 . Method for defining the effect of a psychoactive drug to desired levels of sensitivity and accuracy, the method comprising the steps of: 
       using one or more algorithms to, in respect of each of a plurality of EEG snippets from a subject, allocate to each snippet a class selected from one or more class, each class being associated with a level of drug impairment,
 the one or more algorithms each being defined by a model and a set of parameters and having been previously trained with a plurality of labelled EEG snippets, the labelled snippets including, for each of the classes, at least one snippet associated with a test subject experiencing the level of drug impairment associated with said each class, 
 creating a statistical model which provides the desired levels of sensitivity and accuracy based upon the proportion of snippets from test subjects having the known level of drug effect that falls within the associated class; and 
 
       defining the effect of the drug upon the subject by applying the statistical model to the classes allocated to the snippets of the subject. 
     
     
         2 . A method according to  claim 1 , wherein the snippet length is about 5000 milliseconds. 
     
     
         3 . A method according to  claim 1 , wherein the one or more algorithms is a plurality of unique algorithms. 
     
     
         4 . A method according to  claim 1 , wherein the one or more algorithms is a plurality of machine learning algorithms. 
     
     
         5 . Method for use with a psychoactive drug, the method comprising the steps of: 
       using a plurality of unique algorithms to, in respect of each of a plurality of EEG snippets, allocate to each snippet a class selected from a pair of classes, only one of the pair of classes being associated with use of the drug, each snippet being of a predetermined duration, the algorithms each being defined by a model and a set of parameters and having been previously trained with a plurality of labelled EEG snippets of the same predetermined duration, the labelled snippets including at least one snippet associated with a subject experiencing the psychoactive effect of the drug and labelled as the one of the pair of classes and at least one snippet associated with the subject when under no influence of the drug and labelled as the other of the pair of classes; 
       in respect of each snippet of the plurality of EEG snippets, making a finding of psychoactive effect if the one class is allocated to said each snippet in a proportion of the total classifications of the snippet that meets or exceeds a predetermined threshold; and 
       calculating the psychoactive effect of the drug based upon the proportion of EEG snippets found to have psychoactive effect. 
     
     
         6 . Method according to  claim 5 , wherein the plurality of labelled EEG snippets are obtained by segmenting a plurality of EEG recordings. 
     
     
         7 . Method according to  claim 6 , wherein the plurality of labelled EEG snippets are that which remain after an EEG recording has been segmented and segments determined to be unreliable or noisy through conventional filtering techniques have been removed. 
     
     
         8 . Method according to  claim 6 , wherein the plurality of algorithms includes algorithms based upon each of a plurality of models. 
     
     
         9 . Method according to  claim 8 , wherein, in respect of each model of the plurality, a plurality of algorithms are defined, each of such plurality having a unique set of parameters. 
     
     
         10 . Method according to  claim 5 , wherein the algorithms are machine learning algorithms. 
     
     
         11 . Method according to  claim 5 , wherein 
       the labelled snippets include snippets associated with a plurality of subjects; and 
       the labelled snippets include, in respect of each of the subjects, a plurality of snippets associated with the subject when the subject is experiencing the psychoactive effect of the drug and a plurality of snippets associated with the subject when under no influence of the drug. 
     
     
         12 . Apparatus comprising: 
       apparatus for collecting EEG data from a test subject and segmenting same into segmented EEG test data; and 
       a computing facility adapted to carry out the method of  claim 5 , wherein the plurality of EEG snippets used in the method are defined by the segmented EEG test data. 
     
     
         13 . Apparatus comprising: 
       apparatus for collecting EEG data from a test subject, segmenting same into segmented EEG test data and removing therefrom, using conventional filtering techniques, segments determined to be one or more of unreliable and noisy, thereby producing filtered segments; and 
       a computing facility adapted to carry out the method of  claim 5 , wherein the plurality of EEG snippets used in the method are defined by the filtered segments. 
     
     
         14 . Method for use in a workplace, the method comprising the steps of: 
       in respect of a plurality of performances of a task, each task being permed by a person, in circumstances such that the task is performed by persons under varying levels of drug influence, capturing EEG data of the persons during the performance of the task; 
       choosing one of the performances as the minimum level of competence permissible in the workplace based upon an assessment of the performance for competency; and 
       using the method of  claim 5  to determine the extent to which the performer of the chosen task was under the influence of the drug and the permissible extent of drug influence in the workplace for persons responsible for carrying out the task. 
     
     
         15 . Method for labelling a dosage of a psychoactive drug, the method comprising the step of: 
       administering a dose of the drug to a person; 
       collecting EEG data from the person, at one or more time intervals after intake; 
       using the method of  claim 5  to calculate the maximum psychoactive effect the person experienced following the dose, the time of onset of the dose, where this time is the time required for the psychoactive effect to reach a threshold and the duration of the dose, where this time is the time between when the dose is administered and when the psychoactive effect recedes below the threshold and 
       labelling the dose with the calculated maximum, onset time, and duration. 
     
     
         16 . Method according to  claim 15 , wherein the threshold is 0.35.